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Epistasis pp 35-45 | Cite as

Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits

  • Li MaEmail author
  • Alon Keinan
  • Andrew G. Clark
Part of the Methods in Molecular Biology book series (MIMB, volume 1253)

Abstract

While the importance of epistasis is well established, specific gene–gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein–protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene–gene interactions play in connecting genotypes and complex phenotypes in future GWAS.

Key words

Epistasis Gene–Gene Interaction Biological Knowledge GWAS Lipid 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Animal and Avian SciencesUniversity of MarylandCollege ParkUSA
  2. 2.Department of Biological Statistics and Computational Biology, Cornell Center for Comparative and Population GenomicsCornell UniversityIthacaUSA
  3. 3.Department of Molecular Biology and GeneticsCornell UniversityIthacaUSA

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